Applying deep learning method, the researchers are aiming to "teach" computer to recognise malignant lesions, which would allow at least partially automatize and enhance the accuracy of diagnosing breast cancer.

In 2014, around 93.5 thousand people died from breast cancer in the EU, the vast majority (92.5 thousand) of them were women. Among women, breast cancer accounted for 3.7 % of all deaths. According to the World Health Organisation, more than 1 million new breast cancer cases are being diagnosed every year. The international community of medical professionals is warning that the incidence of oncological diseases is rising; in the last 15 years in Lithuania the cancer rate increased by 75%.

For better treatment and prognosis of cancer patients, early diagnosis is the key.

"Often in cancer diagnosis oncologists rely on visual information - the image of the tissue in question is being analysed in order to determine the nature of the lesions. This process is time consuming and the probability of mistake is not eliminated, which, in the case of cancer can be fatal. By developing mathematical methods for cancer diagnosis we aim to at least partially automatize the diagnosing procedure and to minimize the occurrence of mistakes", says Dr. Tomas Iešmantas, postdoctoral researcher at Kaunas University of Technology (KTU).

For diagnosing breast cancer, he has adapted capsule neural network method introduced by the British researcher Geoffrey Hinton, one of the founding fathers of deep learning (machine learning method).

Dr. Iešmantas, together with his postdoctoral research supervisor Professor Robertas Alzbutas, have analysed 100 microscope images of breast tissue provided by the University of Porto, Portugal. There were 4 types of images in the sample: those of non-cancerous tissue, of non-malignant tumour tissue, of non-invasive and invasive carcinomas. The aim of the investigation was to design a mathematical method for classifying the images into the 4 types mentioned.

"The early results are very promising - we have achieved 85% accuracy rate", says KTU researcher.

He will introduce the results of the research in the 15th International Conference on Image Analysis and Recognition in Portugal. According to Dr. Iešmantas, although the ways of application of mathematical methods in medicine have expanded in recent years, and computers are being taught to diagnose lesions in lungs, to recognise metastasis in lymph nodes, and to localise brain tumours, it is not very likely that cancer diagnosing process will become fully automatized in the near future.

"The research is not only conducted on theoretical level, there are some cases where these methods have already been applied in clinical practice. Even though digitalisation will not replace human judgement, I believe that automatized computer diagnosis will become more common with time and will help to more accurately identify and diagnose certain types of cancer", says Dr. Iešmantas.

Tucson, AZ (Scicasts) — Recent headlines have cast suspicion on social network analysis, which can mine data from the internet to target advertisements or potentially influence elections.

But what if we could use those same tools not for the economic or political gain of a few, but for the health of all humankind? Scientists now can harness the tools of social network analysis to understand connections among genes, an advance that someday could lead to medical advancements.

Dr. Megha Padi, director of the UA Cancer Center Bioinformatics Shared Resource and an assistant professor of molecular and cellular biology, developed a computer algorithm called ALPACA that reveals which gene networks are activated in a diseased cell -- an approach that could lead to better treatments for various diseases. The results were published online April 19 in the open-access Nature Partner journal Systems Biology and Applications.

Cancer researchers usually focus on specific genes when comparing healthy cells to tumour cells, an approach that does not completely explain what occurs behind the scenes to cause cancer.

"You can get a list of the parts in your car, but you won't understand what makes the car run until you understand how all the parts are connected to each other," Dr. Padi said.

Likewise, it is essential to study how genes work together as part of a larger network, so Dr. Padi is analyzing these gene communities in the same way one would examine a social network composed of connections among people who know one another.

Dr. Padi is the first author on the study, in collaboration with John Quackenbush, PhD, director of the Center for Cancer Computational Biology at Dana-Farber Cancer Institute. The study was conducted when she was a postdoctoral fellow at Dana-Farber; Dr. Padi joined the UA in January 2018.

Genes in a community, like people in a social network, talk to one another. In a healthy cell, gene communities function like factory workers, cooperating to process raw materials into goods the cell needs to thrive. In a diseased cell, miscommunication along the "assembly line" results in defective products. Tracking how genes' conversations change over time might provide clues how cancer arises. These conversations can be analyzed using tools developed to study social networks.

"A classic example is a phone network," said Dr. Padi. "I'm calling my mom, my mom may be calling my sister, my sister's calling me, and I may call you, but you won't call my mom. Natural communities are formed, like your work community and family community."

Dr. Quackenbush added, "In the same way, we see that gene regulatory networks form communities. The pattern of 'conversations' within the communities change between healthy and diseased individuals. ALPACA is the first method to understand how the cell's 'social network' is reorganized in disease, which might provide clues how cancer forms."

To uncover cancer's causes, the challenge is to find the differences between gene communities in healthy cells compared to diseased cells, rather than the differences between individual genes. But comparing gene communities is easier said than done, as the genetics underlying cancer can have tens of thousands of interacting components to sort through. Drawing a diagram of these interactions results in what researchers call a "hairball."

"To make a map that humans can understand, scientists need computers to figure out the subtle ways in which this 'hairball' goes awry in tumor cells," Dr. Padi said. One of the next steps is to identify drug candidates that can be further investigated in the laboratory.

"We'd like to use what we have learned to develop new strategies that can help prevent or cure disease," said Dr. Quackenbush.

Dr. Padi is particularly interested in using ALPACA to find novel treatments for people whose cancers fail to respond to currently available treatments. By contrasting cancers that cannot be treated with drugs, known as chemoresistant tumours, with chemosensitive tumours, those that can be treated with drugs, researchers may be able to zero in on a community of "bad guys" -- genetic pathways that might be targeted with customized drugs.

ALPACA's incorporation of social network analysis is an innovative use of tools most commonly associated with marketing, not medical research.

"Network scientists usually ask questions like how information is being spread through Twitter or other communication channels," Dr. Padi said. "We're asking completely different questions, like how networks function in different types of tumours. This type of research is rare because not many people work on both those fields at the same time."

Article adapted from a University of Arizona Health Sciences news release.

Boulder, CO (Scicasts) — Researchers have found that in several samples of advanced differentiated and anaplastic thyroid cancer, mechanisms meant to repair faulty DNA had been broken; in addition to identification of specific genes that may drive these cancers and thus provide attractive targets for treatment.

These broken repair mechanisms led to a subset of thyroid cancers accumulating a high number of genetic alterations - and this "high mutation burden" is a marker recognized by the FDA to recommend treatment with anti-cancer immunotherapies.

"Anaplastic thyroid cancer is a particularly terrible cancer - people wonder what makes it so bad, and advanced thyroid cancer causes significant morbidity. I've had a very productive relationship with Foundation Medicine, primarily to study rare salivary gland cancers and I'm pleased that we've been able to extend our collaboration to the study of thyroid cancers to hopefully answer some of these questions," says Dr. Daniel Bowles, clinical and translational investigator at CU Cancer Center and Head of Cancer Research at the Denver Veterans Administration Medical Center.

Bowles worked with first author Dr. Nikita Pozdeyev to analyze tumour samples submitted by oncologists from around the United States to Foundation Medicine for genetic analysis that could inform treatment strategies. Interestingly, the fact that clinicians who submitted these samples were specifically seeking possible treatment strategies meant that the majority of samples were from advanced cancers.

"Genetic analysis of early-stage thyroid cancers is most often not necessary - we successfully treat these tumours with surgery and radioactive iodine," Pozdeyev says. "But with distant metastases, genetic information becomes important for treatment. Because oncologists had sought this genetic information, our study is enriched for advanced cases."

The researchers point out that even large treatment centers are likely to only a few of these most dangerous, anaplastic thyroid cancers every year. Due to the current study's industry-academia collaboration, the researchers were able to explore 196 of these anaplastic thyroid cancers, "giving us sufficient analytical power to use machine learning and statistical analysis to make sense of the data," Pozdeyev says.

In addition to finding that some anaplastic thyroid cancers carried a high overall mutation burden that could make immunotherapy an attractive treatment option, the group found specific genetic changes driving anaplastic cancers, including amplifications of the genes KDR, KIT and PDGFRA. These genes encode a kind of on-off switch called "receptor tyrosine kinases" that many cancer cells use to speed their growth and proliferation. In this case, these receptor tyrosine kinases happen to be targeted by the drug lenvatinib, which earned FDA approval for use in kidney cancer.

In collaboration with Drs. Bryan Haugen and Rebecca Schweppe, the researchers treated a cohort of thyroid cancer cell lines with lenvatinib, finding that it was the cell line with amplification of KDR, KIT and PDGFRA that was especially sensitive to the drug, hinting that treatment with lenvatinib may be an attractive strategy against a subset of anaplastic thyroid cancers.

"As a clinician, I learn from this study that every patient with advanced thyroid cancer that we consider for systemic therapy should be genotyped - knowledge of genetic background may affect how we treat that patient," Pozdeyev says. "There are many drugs targeting many genetic changes that are approved for other cancers, which we would not usually think to use in thyroid cancer. Some of the findings in this paper will potentially change that."

Article adapted from a University of Colorado Anschutz Medical Campus news release.

St. Louis, MO (Scicasts) — Taking a biopsy of a brain tumour is a complicated and invasive surgical process, but a team of researchers at Washington University is developing a way that allows them to detect tumour biomarkers through a simple blood test.

Hong Chen, a biomedical engineer, and Dr. Eric C. Leuthardt, a neurosurgeon, led a team of engineers, physicians and researchers who have developed a groundbreaking, proof-of-concept technique that allows biomarkers from a brain tumour to pass through the tough blood-brain barrier into a patient's blood using noninvasive focused ultrasound and some tiny bubbles, potentially eliminating the need for a surgical biopsy.

Chen, assistant professor of biomedical engineering in the School of Engineering & Applied Science and of radiation oncology in the School of Medicine, said while researchers have already learned how to get a drug through the blood-brain barrier into the brain via the bloodstream, no one — until now — has found a way to release tumour-specific biomarkers — in this case, messenger RNA (mRNA) — from the brain into the blood.

"I see a clear path for the clinical translation of this technique," said Chen, an expert in ultrasound technology. "Blood-based liquid biopsies have been used in other cancers, but not in the brain. Our proposed technique may make it possible to perform a blood test for brain cancer patients."

The blood test would reveal the amount of mRNA in the blood, which gives physicians specific information about the tumour that can help with diagnosis and treatment options.

Results of the study, which blends imaging, mechanobiology, genomics, immunology, bioinformatics, oncology, radiology and neurosurgery, are published in Scientific Reports April 26, 2018.

Chen; Leuthardt, professor of neurological surgery in the School of Medicine; and researchers from the schools of Engineering and of Medicine, tested their theory in a mouse model using two different types of the deadly glioblastoma brain tumour. They targeted the tumour using focused ultrasound, a technique that uses ultrasonic energy to target tissue deep in the body without incisions or radiation. Similar to a magnifying glass that can focus sunlight to a tiny point, focused ultrasound concentrates ultrasound energy to a tiny point deep into the brain.

Once they had the target — in this case, the brain tumour — researchers then injected microbubbles that travel through the blood similar to red blood cells. When the microbubbles reached the target, they popped, causing tiny ruptures of the blood-brain barrier that allows the biomarkers from the brain tumour to pass through the barrier and release into the bloodstream. A blood sample can determine the biomarkers in the tumour.

This technique could lead to personalized medicine.

"In many ways this has been a holy grail for brain tumour therapy," Leuthardt said. "Having the ability to monitor the changing molecular events of the tumour in an ongoing way allows us to not only better diagnose a tumour in the brain, but to follow its response to different types of treatment."

"Once the blood-brain barrier is open, physicians can deliver drugs to the brain tumour," Chen said. "Physicians can also collect the blood and detect the expression level of biomarkers in the patient. It enables them to perform molecular characterizations of the brain tumour from a blood draw and guide the choice of treatment for individual patients."

In addition, Dr. Gavin Dunn, assistant professor of neurosurgery, a co-author and leader in cancer immunobiology, plans to use the technique with immunotherapy, which offers precision treatment that targets specific biomarkers in the brain.

"This noninvasive focused ultrasound-enabled liquid biopsy technique can be useful for long-term monitoring of brain cancer treatment response, where repeated surgical tissue biopsies may not be feasible," Chen said. "Meanwhile, variations within tumours pose a significant challenge to cancer biomarker research. Focused ultrasound can precisely target different locations of the tumour, thereby causing biomarkers to be released in a spatially-localized manner and allow us to better understand the spatial variations of the tumour and develop better treatment."

The team continues to work to refine the process. The future will require integration with advanced genomic sequencing and bioinformatics to enable even more refined diagnostics. These efforts are being led by co-authors, Allegra Petti, assistant professor of medicine, and Xiaowei Wang, associate professor of radiation oncology.

"Our ongoing work is to optimize the technique and evaluate its sensitivity and safety," Chen said.

Article adapted from a Washington University at St. Louis news release.

Los Angeles, CA (Scicasts) - Over many decades, multiple research studies have sought to understand the dizzying "talk," or interconnectivity, between thousands of microscopic entities in the brain, in particular, neurons. The goal: to one day arrive at a complete brain "mapping" -- a feat that could unlock tremendous therapeutic potential.

Researchers at the University of Southern California Viterbi School of Engineering have developed thin, flexible polymer-based materials for use in microelectrode arrays that record activity more deeply in the brain and with more specific placement than ever before. What's more is that each microelectrode array is made up of eight "tines," each with eight microelectrodes which can record from a total 64 subregions of the brain at once.

In addition, the polymer-based material, called Parylene C, is less invasive and damaging to surrounding cells and tissue than previous microelectrode arrays comprised of silicon or microwires. However, the long and thin probes can easily buckle upon insertion, making it necessary to add a self-dissolving brace made up of polyethylene glycol (PEG) that shortens the array and prevents it from bending.

Professor Ellis Meng of the USC Viterbi Department of Biomedical Engineering and Michelson Center for Convergent Bioscience said that the performance of the new polymer-based material is on par with microwires in terms of recording fidelity and sensitivity. "The information that we can get out is equivalent, but the damage is much less," Meng said. "Polymers are gentler on the brain, and because of that, these devices get recordings of neuronal communication over long periods of time."

As with any prosthetic implant, caution must be exercised in terms of the body's natural immune response to a foreign element. In addition to inflammation, previous microelectrode brain implants made of silicon or microwire have caused neuronal death and glial scarring, which is damage to connective tissue in the nervous system. However, Parylene C is biocompatible and can be microfabricated in extremely thin form that molds well to specific sub-regions of the brain, allowing for exploration with minimal tissue displacement and cell damage.

So far, these arrays have been used to record electrophysiological responses of individual neurons within the hippocampus, a subregion of the brain responsible for memory formation. If injured, the hippocampus may be compromised, resulting in a patient's inability to form new memories. Meng said that the polymer-based material can conform to a specific location in the hippocampus and "listen in on a conversation" between neurons and because there are many such "eavesdroppers" (the microelectrodes), much more information about neural interconnectivity can be gleaned.

"I can pick where I want my electrodes to be, so I can match up to the anatomy of the brain," Meng said. "Along the length of a tine, I can put a group of electrodes here and a group of electrodes there, so if we plant to a certain depth, it's going to be near the neurons I want to record from."

Future research will determine the recording lifetime of polymer-based arrays and their long-term "signal-to-noise" (SNR) stability. Also, the team plans to create devices with even higher density, including a double-sided microelectrode array with 64 electrodes per tine instead of eight -- making for a total of around 4,000 electrodes placed in the brain at once.

Article adapted from a University of Southern California news release.